heston

Financial Derivatives Calculator with 171+ Models (Options Calculator)

Vanilla and exotic option pricing library to support quantitative R&D. Focus on pricing interesting/useful models and contracts (including and beyond Black-Scholes), as well as calibration of financial models to market data.

Option pricing function for the Heston model based on the implementation by Christian Kahl, Peter Jäckel and Roger Lord. Includes Black-Scholes-Merton option pricing and implied volatility estimation. No Financial Toolbox required.

Quant. Research project - Cutting-Edge project (In collaboration with Milliman & University of Paris-Saclay)

The project aims to compare the effectiveness of the Heston model and WGAN-GP in modeling financial time series data.

Heston model implementation with Monte Carlo simulation for stochastic volatility pricing.Implementation of the Heston Stochastic Volatility Model in Python along with it's Monte Carlo Simulation

Sixteen option pricers over six stochastic models including Heston, Bates, SABR and rough Bergomi, spanning analytic, lattice, finite-difference, COS Fourier and Monte Carlo methods.

SPX option pricing as imaginary-time quantum mechanics: arbitrage-free local vol in C++, backtested against SVI, Heston and the practitioner smile on 1.2M quotes

Differentiable quantitative finance library in PyTorch: classical and stochastic-vol pricing, autodiff Greeks, calibration, GPU Monte Carlo (CUDA), arbitrage-penalized neural volatility surfaces

Derivatives pricing library: Black-Scholes-Merton (16 Greeks), Monte Carlo with variance reduction, Asian/Barrier/Lookback/Digital exotics, Heston (Fourier pricing, QE simulation, SPX calibration), rough Bergomi (hybrid scheme). 1,013 tests, QuantLib-validated, 97% coverage.

Reproduction of J.P. Morgan's Deep Hedging papers (2019 pathwise, 2022 risk-averse actor-critic), checked number by number

Heston stochastic-volatility calibration for option pricing, comparing classical optimization and deep-learning methods on SPX implied-volatility data.

Rust library for option pricing: Binomial, Black-Scholes, Heston and intrinsic value, with Least Squares Monte Carlo and random-forest regression for American exercise.

High-performance options Greeks engine — Black-Scholes/Heston, AVX2-vectorised pricing, Google Benchmark suite

Calculer le prix du droit d'acheter ou de vendre plus tard, puis vérifier que les méthodes retrouvent des prix connus et annoncent une erreur réaliste.

Synthetic financial time series with known ground truth — GBM, Heston, Merton jumps, GARCH, regime-switching. Stress-test ML/RL models before deploying.

FFT, Monte Carlo, and finite difference

Crypto options pricing and volatility-surface engine: Black-Scholes/Heston/Merton/SABR, Gatheral SVI calibration, model-free no-arbitrage checks, and mispricing signals netted against costs.

Options pricing done to a quant standard: Black-Scholes + full Greeks, implied-vol solver, binomial/American, Monte-Carlo, exotics (Asian/Barrier/American-LSM), Heston stochastic vol (Fourier + MC) and an SVI surface — validated to ~1e-10 against published references. With a Streamlit dashboard.